
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt


data = pd.read_csv('daily_engagement_full.csv')


data.head()


data.columns

Index(['utc_date', 'acct', 'registration_date', 'subscription_start',
       'course_key', 'sibling_key', 'course_title', 'has_visited',
       'total_minutes_visited', 'lessons_completed', 'projects_completed',
       'account_key'],
      dtype='object')


print('Shape of the Dataframe:',data.shape,'\n')

Shape of the Dataframe: (2309239, 12)


data.dtypes

utc_date                  object
acct                       int64
registration_date         object
subscription_start        object
course_key                object
sibling_key               object
course_title              object
has_visited              float64
total_minutes_visited    float64
lessons_completed        float64
projects_completed       float64
account_key               object
dtype: object


%%time
start='\033[1m'
end='\033[0;0m'

Wall time: 0 ns


all_null = data.isnull().sum().sum()
not_null = data.notnull().sum().sum()
print(f"{start}Null values number for all table is {all_null}, while not null values count is {not_null}{end}")

Null values number for all table is 0, while not null values count is 27710868


data.isnull().sum()

utc_date                 0
acct                     0
registration_date        0
subscription_start       0
course_key               0
sibling_key              0
course_title             0
has_visited              0
total_minutes_visited    0
lessons_completed        0
projects_completed       0
account_key              0
dtype: int64


print(f'{start}Summary Of the dataframe:{end}','\n')
print(data.info(),'\n')

Summary Of the dataframe: 

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2309239 entries, 0 to 2309238
Data columns (total 12 columns):
 #   Column                 Dtype  
---  ------                 -----  
 0   utc_date               object 
 1   acct                   int64  
 2   registration_date      object 
 3   subscription_start     object 
 4   course_key             object 
 5   sibling_key            object 
 6   course_title           object 
 7   has_visited            float64
 8   total_minutes_visited  float64
 9   lessons_completed      float64
 10  projects_completed     float64
 11  account_key            object 
dtypes: float64(4), int64(1), object(7)
memory usage: 211.4+ MB
None


print(f'{start}Describtion Of the dataframe:{end}','\n')
data.describe()

Describtion Of the dataframe:


describ_cols = data.describe().columns


describ_cols

Index(['acct', 'has_visited', 'total_minutes_visited', 'lessons_completed',
       'projects_completed'],
      dtype='object')


data_describtion = data.describe().transpose()


data_describtion


data_describtion.columns

Index(['count', 'mean', 'std', 'min', '25%', '50%', '75%', 'max'], dtype='object')


data_describtion.dtypes

count    float64
mean     float64
std      float64
min      float64
25%      float64
50%      float64
75%      float64
max      float64
dtype: object


def data_skewness(df, mean, median, lst_of_cols):
    for i, m in (enumerate(df[mean])):
        for j, med in (enumerate(df[median])):
            if i == j:
                if int(m) > int(med):
                    print('Mean > Median')
                    print(f'{lst_of_cols[i]}: rightly skewed\n\n')
                
                elif int(m) < int(med):
                    print('Mean < Median')
                    print(f'{lst_of_cols[i]}: leftly skewed\n\n')
                
                elif int(m) == int(med):
                    print('Mean ~ Median')
                    print(f'{lst_of_cols[i]}: almost Normal Distributed\n\n')


data_skewness(data_describtion, 'mean', '50%', describ_cols)

Mean > Median
acct: rightly skewed


Mean ~ Median
has_visited: almost Normal Distributed


Mean > Median
total_minutes_visited: rightly skewed


Mean ~ Median
lessons_completed: almost Normal Distributed


Mean ~ Median
projects_completed: almost Normal Distributed


def box_plots(df, cols_lst):
    # boxplot all the numerical columns and see if there any outliers
    for i in cols_lst:
        df.iloc[:, 1:].boxplot(column=i)
        plt.title(f"Box Plot Of {i}" , fontsize=20, color="orange")
        plt.savefig(f'{i}.png', facecolor='w', bbox_inches="tight", pad_inches=0.3, transparent=False)
        plt.show()


box_plots(data, describ_cols)


def count_plot(df, cols_lst):
    for i in cols_lst:
        plt.figure(figsize=(12,6))
        plt.xticks(rotation = 45)
        plt.title(f"Analysis of {i}" , fontsize=20, color="orange")
        plt.savefig(f'Analysis of {i}.png', facecolor='w', bbox_inches="tight", pad_inches=0.3, transparent=False)
        sns.countplot(df[i], color='green')
        plt.show()


count_plot(data, describ_cols)

D:\Programs\Anaconda2\lib\site-packages\seaborn\_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  FutureWarning

D:\Programs\Anaconda2\lib\site-packages\seaborn\_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  FutureWarning

D:\Programs\Anaconda2\lib\site-packages\seaborn\_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  FutureWarning

---------------------------------------------------------------------------
KeyboardInterrupt                         Traceback (most recent call last)
~\AppData\Local\Temp\ipykernel_11492\634224666.py in <module>
----> 1 count_plot(data, describ_cols)

~\AppData\Local\Temp\ipykernel_11492\4205224597.py in count_plot(df, cols_lst)
      5         plt.title(f"Analysis of {i}" , fontsize=20, color="orange")
      6         plt.savefig(f'Analysis of {i}.png', facecolor='w', bbox_inches="tight", pad_inches=0.3, transparent=False)
----> 7         sns.countplot(df[i], color='green')
      8         plt.show()

D:\Programs\Anaconda2\lib\site-packages\seaborn\_decorators.py in inner_f(*args, **kwargs)
     44             )
     45         kwargs.update({k: arg for k, arg in zip(sig.parameters, args)})
---> 46         return f(**kwargs)
     47     return inner_f
     48 

D:\Programs\Anaconda2\lib\site-packages\seaborn\categorical.py in countplot(x, y, hue, data, order, hue_order, orient, color, palette, saturation, dodge, ax, **kwargs)
   3608         ax = plt.gca()
   3609 
-> 3610     plotter.plot(ax, kwargs)
   3611     return ax
   3612 

D:\Programs\Anaconda2\lib\site-packages\seaborn\categorical.py in plot(self, ax, bar_kws)
   1637     def plot(self, ax, bar_kws):
   1638         """Make the plot."""
-> 1639         self.draw_bars(ax, bar_kws)
   1640         self.annotate_axes(ax)
   1641         if self.orient == "h":

D:\Programs\Anaconda2\lib\site-packages\seaborn\categorical.py in draw_bars(self, ax, kws)
   1603             # Draw the bars
   1604             barfunc(barpos, self.statistic, self.width,
-> 1605                     color=self.colors, align="center", **kws)
   1606 
   1607             # Draw the confidence intervals

D:\Programs\Anaconda2\lib\site-packages\matplotlib\__init__.py in inner(ax, data, *args, **kwargs)
   1410     def inner(ax, *args, data=None, **kwargs):
   1411         if data is None:
-> 1412             return func(ax, *map(sanitize_sequence, args), **kwargs)
   1413 
   1414         bound = new_sig.bind(ax, *args, **kwargs)

D:\Programs\Anaconda2\lib\site-packages\matplotlib\axes\_axes.py in bar(self, x, height, width, bottom, align, **kwargs)
   2407             elif orientation == 'horizontal':
   2408                 r.sticky_edges.x.append(l)
-> 2409             self.add_patch(r)
   2410             patches.append(r)
   2411 

D:\Programs\Anaconda2\lib\site-packages\matplotlib\axes\_base.py in add_patch(self, p)
   2356         if p.get_clip_path() is None:
   2357             p.set_clip_path(self.patch)
-> 2358         self._update_patch_limits(p)
   2359         self._children.append(p)
   2360         p._remove_method = self._children.remove

D:\Programs\Anaconda2\lib\site-packages\matplotlib\axes\_base.py in _update_patch_limits(self, patch)
   2391         trf_to_data = patch_trf - self.transData
   2392         xys = trf_to_data.transform(vertices)
-> 2393         self.update_datalim(xys, updatex=updatex, updatey=updatey)
   2394 
   2395     def add_table(self, tab):

D:\Programs\Anaconda2\lib\site-packages\matplotlib\axes\_base.py in update_datalim(self, xys, updatex, updatey)
   2477             return
   2478         self.dataLim.update_from_data_xy(xys, self.ignore_existing_data_limits,
-> 2479                                          updatex=updatex, updatey=updatey)
   2480         self.ignore_existing_data_limits = False
   2481 

D:\Programs\Anaconda2\lib\site-packages\matplotlib\transforms.py in update_from_data_xy(self, xy, ignore, updatex, updatey)
    967         path = Path(xy)
    968         self.update_from_path(path, ignore=ignore,
--> 969                               updatex=updatex, updatey=updatey)
    970 
    971     @BboxBase.x0.setter

D:\Programs\Anaconda2\lib\site-packages\matplotlib\transforms.py in update_from_path(self, path, ignore, updatex, updatey)
    900                 self._minpos[0] = minpos[0]
    901             if updatey:
--> 902                 self._points[:, 1] = points[:, 1]
    903                 self._minpos[1] = minpos[1]
    904 

KeyboardInterrupt:


print(data['acct'].unique())
print(len(data['acct'].unique()))

[448  44 258 ... 874 754 854]
1237


print(data['account_key'].unique())
print(len(data['account_key'].unique()))

['2257038596' '59503937' '1603920644' ... '4804719296' '3567109205'
 'u26334020']
1237


print(f"The number of students: {len(data['account_key'].unique())}")

The number of students: 1237


for col in data.columns:
    #print(f'unique_vals: {data[col].unique()}')
    print(f'column_name: {col}, \n\tnum_of_unique_vals: {len(data[col].unique())}, \n{data[col].value_counts()}', end='\n\n')

column_name: utc_date, 
	num_of_unique_vals: 295, 
2015-07-10    22860
2015-07-11    22752
2015-07-09    22716
2015-08-23    12369
2015-08-25    12274
              ...  
2014-11-08       15
2014-11-07       15
2014-11-06       15
2014-11-09       15
2014-11-05       15
Name: utc_date, Length: 295, dtype: int64

column_name: acct, 
	num_of_unique_vals: 1237, 
1244    7098
442     6518
448     4858
604     4783
568     4783
        ... 
1301      15
1043      15
890       15
908       15
1047      15
Name: acct, Length: 1237, dtype: int64

column_name: registration_date, 
	num_of_unique_vals: 687, 
2014-11-13    66784
2014-12-05    53848
2014-10-07    50461
2014-11-12    36944
2014-11-10    30960
              ...  
2014-10-25       18
2014-12-30       17
2014-06-23       15
2012-02-01       15
2012-02-21       15
Name: registration_date, Length: 687, dtype: int64

column_name: subscription_start, 
	num_of_unique_vals: 115, 
2014-11-10    302388
2015-03-10    147346
2014-11-14     81253
2015-04-08     80870
2015-04-01     80297
               ...  
2015-08-16       266
2015-08-20       228
2014-12-29       210
2015-08-24        95
2014-11-05        75
Name: subscription_start, Length: 115, dtype: int64

column_name: course_key, 
	num_of_unique_vals: 19, 
ud359-nd     138418
ud120        138418
nd002        138418
ud804        138418
ud304        138418
ud507        138418
ud032        138418
ud120-nd     138418
ud359        138418
ud304-nd     138418
ud032-nd     138418
ud804-nd     138418
ud651        138418
ud507-nd     138418
ud651-nd     138418
ud257-nd      89053
ud257         89053
ud134-nd      36308
ud134a-nd     18555
Name: course_key, dtype: int64

column_name: sibling_key, 
	num_of_unique_vals: 11, 
ud359     276836
ud120     276836
ud651     276836
ud507     276836
ud804     276836
ud032     276836
ud304     276836
ud257     178106
nd002     138418
ud134      36308
ud134a     18555
Name: sibling_key, dtype: int64

column_name: course_title, 
	num_of_unique_vals: 11, 
Intro to Data Science           276836
Intro to Machine Learning       276836
Data Analysis with R            276836
Data Visualization and D3.js    276836
JavaScript Basics               276836
Data Wrangling with MongoDB     276836
Intro to HTML and CSS           276836
A/B Testing                     178106
Data Analyst Nanodegree         138418
ud134-nd                         36308
ud134a-nd                        18555
Name: course_title, dtype: int64

column_name: has_visited, 
	num_of_unique_vals: 2, 
0.0    2262773
1.0      46466
Name: has_visited, dtype: int64

column_name: total_minutes_visited, 
	num_of_unique_vals: 45844, 
0.000000     2262773
9.252010           2
91.294271          2
78.330007          2
44.523067          2
              ...   
9.069478           1
68.674106          1
6.371004           1
87.959215          1
42.222797          1
Name: total_minutes_visited, Length: 45844, dtype: int64

column_name: lessons_completed, 
	num_of_unique_vals: 12, 
0.0     2295819
1.0       10435
2.0        2238
3.0         513
4.0         145
5.0          50
6.0          25
7.0           6
8.0           5
16.0          1
10.0          1
9.0           1
Name: lessons_completed, dtype: int64

column_name: projects_completed, 
	num_of_unique_vals: 2, 
0.0    2308322
1.0        917
Name: projects_completed, dtype: int64

column_name: account_key, 
	num_of_unique_vals: 1237, 
993208778     7098
u49383157     6518
2257038596    4858
u42308719     4783
2773048546    4783
              ... 
4140598645      15
u141065         15
1404998541      15
3367788599      15
1604718744      15
Name: account_key, Length: 1237, dtype: int64


dict_col1 = dict(data['acct'].value_counts())


len(dict_col1)

1237


data1 = pd.DataFrame.from_dict(dict_col1, orient='index').T
data1


def plotData_subplot(df, sub_title, col1, col2):
    fig, axes = plt.subplots(1, 2, figsize=(11, 6))
    fig.suptitle(sub_title, fontsize=16)
    
    dict_col1 = dict(df[col1].value_counts())
    dict_col2 = dict(df[col2].value_counts())
    
    data1 = pd.DataFrame.from_dict(dict_col1, orient='index').T
    data2 = pd.DataFrame.from_dict(dict_col2, orient='index').T
    
    data1.plot(kind='bar', ax=axes[0])
    data2.plot(kind='bar', ax=axes[1])
    
    axes[0].set_title(col1)
    axes[1].set_title(col2)
    plt.show()


plotData_subplot(data, 'acct vs account_key','acct', 'account_key')


def plotData(df, col):
    dict_col = dict(df[col].value_counts())
    data = pd.DataFrame.from_dict(dict_col, orient='index').T
    data.plot(kind='bar')
    
    plt.xlabel(col)
    plt.show()


plotData(data, 'acct')


plotData(data, 'account_key')


data.duplicated().sum()

34164


(~data.duplicated()).sum()

2275075


# Extract duplicate rows
data.loc[data.duplicated(), :]


data.drop_duplicates(inplace=True)


data.shape

(2275075, 12)


#def compare_vals(df, col1, col2):


# take two columns and returns true if each colum represent the same relation that the other column represents 
def represent_same(df, col1, col2):
    dict_col1 = dict(df[col1].value_counts())
    dict_col2 = dict(df[col2].value_counts())

    check = [True for i, x in enumerate(dict_col1) for j, y in enumerate(dict_col2) if i==j and dict_col1[x]==dict_col2[y]]

    check = set(check)
    if len(check)==1:
        return True
    return False


same = represent_same(data, 'acct', 'account_key')


print(same)

True


data.drop('account_key', inplace=True, axis=1)


data.rename(columns={'acct':'account_key'}, inplace=True)


data


data.dtypes

utc_date                  object
account_key                int64
registration_date         object
subscription_start        object
course_key                object
sibling_key               object
course_title              object
has_visited              float64
total_minutes_visited    float64
lessons_completed        float64
projects_completed       float64
dtype: object


represent_same(data, 'utc_date', 'registration_date')

False


represent_same(data, 'utc_date', 'subscription_start')

False


represent_same(data, 'registration_date', 'subscription_start')

False


data['utc_date'] = pd.to_datetime(data['utc_date'])
data['registration_date'] = pd.to_datetime(data['registration_date'])
data['subscription_start'] = pd.to_datetime(data['subscription_start'])


data.dtypes

utc_date                 datetime64[ns]
account_key                       int64
registration_date        datetime64[ns]
subscription_start       datetime64[ns]
course_key                       object
sibling_key                      object
course_title                     object
has_visited                     float64
total_minutes_visited           float64
lessons_completed               float64
projects_completed              float64
dtype: object


data.dtypes

utc_date                 datetime64[ns]
account_key                       int64
registration_date        datetime64[ns]
subscription_start       datetime64[ns]
course_key                       object
sibling_key                      object
course_title                     object
has_visited                     float64
total_minutes_visited           float64
lessons_completed               float64
projects_completed              float64
dtype: object


data


print(data['course_title'].unique())
print(len(data['course_title'].unique()))

['Intro to Data Science' 'Intro to Machine Learning'
 'Data Analysis with R' 'Data Visualization and D3.js' 'JavaScript Basics'
 'Data Wrangling with MongoDB' 'Intro to HTML and CSS'
 'Data Analyst Nanodegree' 'A/B Testing' 'ud134-nd' 'ud134a-nd']
11


print(data['sibling_key'].unique())
print(len(data['sibling_key'].unique()))

['ud359' 'ud120' 'ud651' 'ud507' 'ud804' 'ud032' 'ud304' 'nd002' 'ud257'
 'ud134' 'ud134a']
11


print(data['course_key'].unique())
print(len(data['course_key'].unique()))

['ud359-nd' 'ud120-nd' 'ud651-nd' 'ud507-nd' 'ud651' 'ud804-nd' 'ud032-nd'
 'ud304-nd' 'ud120' 'ud359' 'ud032' 'ud507' 'ud304' 'ud804' 'nd002'
 'ud257-nd' 'ud257' 'ud134-nd' 'ud134a-nd']
19


plotData_subplot(data, 'course_title vs sibling_key','course_title', 'sibling_key')


represent_same(data, 'course_title', 'sibling_key')

True


print(data['course_key'].unique())
print(len(data['course_key'].unique()))

['ud359-nd' 'ud120-nd' 'ud651-nd' 'ud507-nd' 'ud651' 'ud804-nd' 'ud032-nd'
 'ud304-nd' 'ud120' 'ud359' 'ud032' 'ud507' 'ud304' 'ud804' 'nd002'
 'ud257-nd' 'ud257' 'ud134-nd' 'ud134a-nd']
19


user_course = data.groupby(['account_key', 'course_title'])


user_course.first()


course_title_key = data.groupby(['course_title', 'course_key'])


course_title_key.first()


def course_key_dict(df, col1, col2):
    col1_col2 = {}   
    for course in df[col1].unique():
        values = []
        for ck in df[col2][df[col1]==course]:
            values.append(ck)
        col1_col2[course] = set(values)
    
    print(col1_col2)
    print(len(col1_col2))
    return col1_col2


course_title_key = course_key_dict(data, 'course_title', 'course_key')

{'Intro to Data Science': {'ud359-nd', 'ud359'}, 'Intro to Machine Learning': {'ud120', 'ud120-nd'}, 'Data Analysis with R': {'ud651', 'ud651-nd'}, 'Data Visualization and D3.js': {'ud507', 'ud507-nd'}, 'JavaScript Basics': {'ud804', 'ud804-nd'}, 'Data Wrangling with MongoDB': {'ud032-nd', 'ud032'}, 'Intro to HTML and CSS': {'ud304-nd', 'ud304'}, 'Data Analyst Nanodegree': {'nd002'}, 'A/B Testing': {'ud257-nd', 'ud257'}, 'ud134-nd': {'ud134-nd'}, 'ud134a-nd': {'ud134a-nd'}}
11


course_title_sibling = course_key_dict(data, 'course_title', 'sibling_key')

{'Intro to Data Science': {'ud359'}, 'Intro to Machine Learning': {'ud120'}, 'Data Analysis with R': {'ud651'}, 'Data Visualization and D3.js': {'ud507'}, 'JavaScript Basics': {'ud804'}, 'Data Wrangling with MongoDB': {'ud032'}, 'Intro to HTML and CSS': {'ud304'}, 'Data Analyst Nanodegree': {'nd002'}, 'A/B Testing': {'ud257'}, 'ud134-nd': {'ud134'}, 'ud134a-nd': {'ud134a'}}
11


data.drop('sibling_key', inplace=True, axis=1)
data.drop('course_key', inplace=True, axis=1)


data


#plotData_subplot(data, 'course_title vs course_key', 'course_title', 'course_key')


#course_key_dict(data, 'account_key', 'course_title')


def stats(df, col1, col2, threshold, title):
    counter = {}   
    total = 0
    for c_key in df[col1].unique():
        cnt=0
        for v in df[col2][df[col1]==c_key]:
            if v>=threshold:
                cnt+=1
        total+=cnt
        counter[c_key] = cnt
    
    #print(counter)
    print(len(counter))
    print(f"{title}: {total}")
    return counter


visited_dict = stats(data, 'course_title', 'has_visited', 1, "Total number of visits for all courses")

11
Total number of visits for all courses: 45865


visited_dict

{'Intro to Data Science': 13958,
 'Intro to Machine Learning': 4699,
 'Data Analysis with R': 5355,
 'Data Visualization and D3.js': 1584,
 'JavaScript Basics': 232,
 'Data Wrangling with MongoDB': 11073,
 'Intro to HTML and CSS': 348,
 'Data Analyst Nanodegree': 7507,
 'A/B Testing': 354,
 'ud134-nd': 716,
 'ud134a-nd': 39}


minutes_dict = stats(data, 'course_title', 'total_minutes_visited', 1, "Total number of visits for all courses")

11
Total number of visits for all courses: 45865


minutes_dict

{'Intro to Data Science': 13958,
 'Intro to Machine Learning': 4699,
 'Data Analysis with R': 5355,
 'Data Visualization and D3.js': 1584,
 'JavaScript Basics': 232,
 'Data Wrangling with MongoDB': 11073,
 'Intro to HTML and CSS': 348,
 'Data Analyst Nanodegree': 7507,
 'A/B Testing': 354,
 'ud134-nd': 716,
 'ud134a-nd': 39}


visited_dict == minutes_dict

True


data.drop('has_visited', inplace=True, axis=1)


data


def stats_study(df, col1, col2, title):
    counter = {}   
    total = 0
    for c_key in df[col1].unique():
        cnt=0
        for v in df[col2][df[col1]==c_key]:
            cnt+=v/60
        total+=cnt
        counter[c_key] = cnt
    
    print(len(counter))
    print(f"{title}: {total} hour")
    return counter


total_study_minutes_per_user = stats_study(data, 'account_key', 'total_minutes_visited', "Total study hour per each student")

1237
Total study hour per each student: 55024.38935129667 hour


total_study_minutes_per_user

{448: 21.173192013890336,
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def plotDict(dictionary, num, xl, yl, title):
    sorted_dict = dict(sorted(dictionary.items(), key=lambda x:x[1]))
    df = pd.DataFrame.from_dict(sorted_dict, orient='index')
    pd_toPlot = pd.concat([df.head(num), df.tail(num)])
    
    #return pd_toPlot
    #pd_toPlot.plot(kind='bar', title=title, figsize=(12, 6))
    #c = ['red', 'yellow', 'black', 'blue', 'orange']
    pd_toPlot.plot(kind='bar', title=title, figsize=(12, 6))

    plt.xlabel(xl)
    plt.ylabel(yl)
    plt.savefig(f'{title}.png', facecolor='w', bbox_inches="tight", pad_inches=0.3, transparent=False)
    plt.show()
    print(pd_toPlot)


plotDict(total_study_minutes_per_user, 5, 'account_key', "hours", "user_studying_hours")

               0
837     0.000000
1117    0.000000
745     0.000000
929     0.000000
756     0.000000
428   302.240125
277   302.869608
198   313.722093
546   329.395111
140   416.522858


total_study_minutes_per_course = stats_study(data, 'course_title', 'total_minutes_visited', "Total study hour per each student")

9
Total study hour per each student: 54047.517509152225 hour


total_study_minutes_per_course

{'Intro to Data Science': 20605.002014807724,
 'Intro to Machine Learning': 6091.752816405661,
 'Data Analysis with R': 6953.553592002642,
 'Data Visualization and D3.js': 1796.9486961333712,
 'JavaScript Basics': 258.1663076249884,
 'Data Wrangling with MongoDB': 14864.731985163948,
 'Intro to HTML and CSS': 343.51694648333955,
 'Data Analyst Nanodegree': 2663.789592283335,
 'A/B Testing': 470.05555824721944}


plotDict(total_study_minutes_per_course, 5, 'course_studied_hours', "hours", "course studying hours")

                                         0
JavaScript Basics               258.166308
Intro to HTML and CSS           343.516946
A/B Testing                     470.055558
Data Visualization and D3.js   1796.948696
Data Analyst Nanodegree        2663.789592
Data Analyst Nanodegree        2663.789592
Intro to Machine Learning      6091.752816
Data Analysis with R           6953.553592
Data Wrangling with MongoDB   14864.731985
Intro to Data Science         20605.002015


unknown_courses_df


unknown_courses = ['ud134a-nd', 'ud134-nd']
unknown_courses_df = data.loc[data['course_title'].isin(unknown_courses)]


data.drop(unknown_courses_df.index, inplace=True)


data


# calculates the sum per any column that the user wants to sum
def stats_study_1(df, col1, col2, title):
    counter = {}   
    total = 0
    for c_key in df[col1].unique():
        cnt=0
        for v in df[col2][df[col1]==c_key]:
            cnt+=v
        total+=cnt
        counter[c_key] = cnt
    
    print(len(counter))
    print(f"{title}: {total}")
    return counter


total_study_lessons_per_course = stats_study_1(data, 'course_title', 'lessons_completed', "Total study hour per each student")

9
Total study hour per each student: 16558.0


plotDict(total_study_lessons_per_course, 5, 'course_studied_lessons', "lessons", "studied lessons per each course")

                                   0
A/B Testing                    133.0
Intro to HTML and CSS          143.0
JavaScript Basics              233.0
Data Analyst Nanodegree        604.0
Data Visualization and D3.js   645.0
Data Visualization and D3.js   645.0
Data Analysis with R          2474.0
Intro to Machine Learning     2499.0
Data Wrangling with MongoDB   4817.0
Intro to Data Science         5010.0


total_done_projects_per_course = stats_study_1(data, 'course_title', 'projects_completed', "Total done projects per each student")

9
Total done projects per each student: 917.0


plotDict(total_done_projects_per_course, 5, "user account key", "projects", 'done_projects')

                                  0
A/B Testing                     0.0
JavaScript Basics               2.0
Intro to HTML and CSS           2.0
Data Visualization and D3.js   36.0
Data Analyst Nanodegree        43.0
Data Analyst Nanodegree        43.0
Intro to Machine Learning      87.0
Data Analysis with R          152.0
Data Wrangling with MongoDB   253.0
Intro to Data Science         342.0


total_study_lessons_per_user = stats_study_1(data, 'account_key', 'lessons_completed', "Total study hour per each student")

1237
Total study hour per each student: 16558.0


plotDict(total_study_lessons_per_user, 5, "user account key", "lessons", 'course_studied_lessons')

         0
448    0.0
943    0.0
837    0.0
1288   0.0
1006   0.0
379   62.0
171   64.0
336   65.0
597   65.0
139   66.0


total_study_projects_per_user = stats_study_1(data, 'account_key', 'projects_completed', "Total study hour per each student")

1237
Total study hour per each student: 917.0


plotDict(total_study_projects_per_user, 5, "user account key", "lessons", 'projects_done')

        0
587   0.0
943   0.0
837   0.0
1288  0.0
1006  0.0
532   6.0
99    6.0
132   6.0
139   6.0
474   8.0


print(data['course_title'].unique())
print(len(data['course_title'].unique()))

['Intro to Data Science' 'Intro to Machine Learning'
 'Data Analysis with R' 'Data Visualization and D3.js' 'JavaScript Basics'
 'Data Wrangling with MongoDB' 'Intro to HTML and CSS'
 'Data Analyst Nanodegree' 'A/B Testing']
9


help(stats)

Help on function stats in module __main__:

stats(df, col1, col2, threshold, title)


stats(data, 'course_title', 'lessons_completed', 1, "course per number of completed lessons")

9
course per number of completed lessons: 12870

{'Intro to Data Science': 3988,
 'Intro to Machine Learning': 1759,
 'Data Analysis with R': 2002,
 'Data Visualization and D3.js': 468,
 'JavaScript Basics': 109,
 'Data Wrangling with MongoDB': 3761,
 'Intro to HTML and CSS': 108,
 'Data Analyst Nanodegree': 595,
 'A/B Testing': 80}


stats(data, 'course_title', 'projects_completed', 1, "course per number of completed projects")

9
course per number of completed projects: 917

{'Intro to Data Science': 342,
 'Intro to Machine Learning': 87,
 'Data Analysis with R': 152,
 'Data Visualization and D3.js': 36,
 'JavaScript Basics': 2,
 'Data Wrangling with MongoDB': 253,
 'Intro to HTML and CSS': 2,
 'Data Analyst Nanodegree': 43,
 'A/B Testing': 0}


pd.DatetimeIndex(data['subscription_start']).year.unique()

Int64Index([2014, 2015], dtype='int64', name='subscription_start')


pd.DatetimeIndex(data['utc_date']).year.unique()

Int64Index([2014, 2015], dtype='int64', name='utc_date')


pd.DatetimeIndex(data['registration_date']).year.unique()

Int64Index([2014, 2013, 2012, 2015, 1816], dtype='int64', name='registration_date')


data['registration_date'][pd.DatetimeIndex(data['registration_date']).year==1816].count()

34


year_1816 = data[pd.DatetimeIndex(data['registration_date']).year==1816]


year_1816


data.drop(year_1816.index, inplace=True)


data


pd.DatetimeIndex(data['registration_date']).year.unique()

Int64Index([2014, 2013, 2012, 2015], dtype='int64', name='registration_date')


data['registration_date'][0].year

2014


data['total_minutes_visited'][pd.DatetimeIndex(data['subscription_start']).year==2014].sum()

1273562.286094507


data['total_minutes_visited'][pd.DatetimeIndex(data['subscription_start']).year==2015].sum()

1969273.1714284602


data['total_minutes_visited'].sum()

3242835.457522968


acct_2014 = data['account_key'][pd.DatetimeIndex(data['subscription_start']).year==2014].unique()


len(acct_2014)

429


acct_2015 = data['account_key'][pd.DatetimeIndex(data['subscription_start']).year==2015].unique()


len(acct_2015)

945


len(data['account_key'].unique())

1237


len(acct_2014) + len(acct_2015) == len(data['account_key'].unique())

False


common_students = [x for x in acct_2014 if x in acct_2015]


len(common_students)

137


users_2014_only = [x for x in acct_2014 if x not in common_students]


len(acct_2014_only)

292


users_2015_only = [x for x in acct_2015 if x not in common_students]


len(users_2015_only)

808


len(common_students) + len(acct_2014_only) + len(users_2015_only) == len(data['account_key'].unique())

True


lazy_acct_2014 = data['account_key'][(pd.DatetimeIndex(data['subscription_start']).year==2014) & (data['total_minutes_visited']==0) & (data['lessons_completed']==0) & (data['projects_completed']==0)].unique()


len(lazy_acct_2014)

429


lazy_acct_2015 = data['account_key'][(pd.DatetimeIndex(data['subscription_start']).year==2015) & (data['total_minutes_visited']==0) & (data['lessons_completed']==0) & (data['projects_completed']==0)].unique()


len(lazy_acct_2015)

945


list(lazy_acct_2014) == list(acct_2014)

True


list(lazy_acct_2015) == list(acct_2015)

True


neglect = data.loc[(data['total_minutes_visited']==0) & (data['lessons_completed']==0) & (data['projects_completed']==0)]


neglect


data.drop(neglect.index, inplace=True)


data


pd.DatetimeIndex(data['subscription_start']).year.unique()

Int64Index([2014, 2015], dtype='int64', name='subscription_start')


len(data['account_key'].unique())

1164


all_students = common_students + users_2014_only + users_2015_only


good_students = list(data['account_key'].unique())


bad_students = [u for u in all_students if u not in good_students]


len(bad_students)

73


bad_students

[837,
 1117,
 745,
 929,
 756,
 1114,
 930,
 683,
 1230,
 1239,
 1166,
 1203,
 865,
 767,
 1293,
 1234,
 907,
 790,
 867,
 1212,
 1074,
 1195,
 1092,
 1035,
 999,
 716,
 1045,
 1100,
 809,
 1066,
 899,
 1267,
 1088,
 729,
 1038,
 796,
 936,
 1265,
 1115,
 863,
 201,
 797,
 939,
 1275,
 1059,
 1192,
 1184,
 731,
 826,
 1266,
 1227,
 1172,
 978,
 951,
 915,
 1178,
 773,
 849,
 856,
 1302,
 879,
 1018,
 998,
 1136,
 816,
 840,
 1228,
 893,
 1028,
 1144,
 880,
 1157,
 874]


data['subscription_start'][pd.DatetimeIndex(data['subscription_start']).year==2015].count()

27866


# ['subscription_start', 'account_key', 'course_title', 'total_minutes_visited', 'lessons_completed', 'projects_completed']


# to get the time spent per user for each of the courses across each year
def users_metadata(df, cols):
    total_yrs = {}
    lst_of_all_cols = []    # will be used in extraction of values from the df using regex
    
    for yr in pd.DatetimeIndex(df[cols[0]]).year.unique():
        total_all = {}
        print(f"\nfor year {yr}:")
        for acct in df[cols[1]].unique():
            courses_hrs = {}
            courses_lesn = {}
            courses_proj = {}
            total_hrs = 0
            for course in df[cols[2]].unique():
                c_hrs = course+'_hrs'
                c_lsns = course+'_lesson'
                c_proj = course+'_proj'
                
                mins = df[cols[3]][(pd.DatetimeIndex(df[cols[0]]).year==yr) & (df[cols[1]]==acct) & (df[cols[2]]==course)].sum()
                lessons = df[cols[4]][(pd.DatetimeIndex(df[cols[0]]).year==yr) & (df[cols[1]]==acct) & (df[cols[2]]==course)].sum()
                projects = df[cols[5]][(pd.DatetimeIndex(df[cols[0]]).year==yr) & (df[cols[1]]==acct) & (df[cols[2]]==course)].sum()
                courses_hrs[c_hrs] = mins/60
                courses_lesn[c_lsns] = lessons
                courses_proj[c_proj] = projects
                total_hrs += mins/60
        
            total_all[acct] = {cols[1]:acct, cols[0]:yr, "total_study_hrs":total_hrs, cols[4]:lessons, cols[5]:projects, 'c_hrs':courses_hrs, 'c_lsns':courses_lesn, 'c_proj':courses_proj}
            #print(total_all[acct])
            print(f"\tuser {acct} done...")
        total_yrs[yr] = total_all
    
    lst_of_all_cols.extend((cols[1], cols[0], "total_study_hrs", cols[4], cols[5]))
    lst_of_cols = [va for va in list(courses_hrs) + list(courses_lesn) + list(courses_proj)]
    lst_of_all_cols = lst_of_all_cols + lst_of_cols
        
    return total_yrs, lst_of_all_cols


metadata, meta_cols = users_metadata(data, ['subscription_start', 'account_key', 'course_title', 'total_minutes_visited', 'lessons_completed', 'projects_completed'])

for year 2014:
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    'A/B Testing_hrs': 0.0,
    'Data Wrangling with MongoDB_hrs': 0.0},
   'c_lsns': {'Intro to Data Science_lesson': 0.0,
    'Intro to HTML and CSS_lesson': 0.0,
    'Data Analyst Nanodegree_lesson': 0.0,
    'Data Visualization and D3.js_lesson': 0.0,
    'Data Analysis with R_lesson': 0.0,
    'JavaScript Basics_lesson': 0.0,
    'Intro to Machine Learning_lesson': 0.0,
    'A/B Testing_lesson': 0.0,
    'Data Wrangling with MongoDB_lesson': 0.0},
   'c_proj': {'Intro to Data Science_proj': 0.0,
    'Intro to HTML and CSS_proj': 0.0,
    'Data Analyst Nanodegree_proj': 0.0,
    'Data Visualization and D3.js_proj': 0.0,
    'Data Analysis with R_proj': 0.0,
    'JavaScript Basics_proj': 0.0,
    'Intro to Machine Learning_proj': 0.0,
    'A/B Testing_proj': 0.0,
    'Data Wrangling with MongoDB_proj': 0.0}},
  ...}}


metadata[2014][160]['c_proj']

{'Intro to Data Science_proj': 1.0,
 'Intro to HTML and CSS_proj': 0.0,
 'Data Analyst Nanodegree_proj': 0.0,
 'Data Visualization and D3.js_proj': 0.0,
 'Data Analysis with R_proj': 0.0,
 'JavaScript Basics_proj': 0.0,
 'Intro to Machine Learning_proj': 0.0,
 'A/B Testing_proj': 0.0,
 'Data Wrangling with MongoDB_proj': 0.0}


metadata[2014][50]['c_proj'] == metadata[2015][50]['c_proj']

False


metadata[2014][50] == metadata[2015][50]

False


users_study_df = pd.DataFrame.from_dict(metadata, orient='index').T


users_study_df


users_study_df.to_csv('cleaned_daily_engagement.csv')


users_study_df.columns

Int64Index([2014, 2015], dtype='int64')


users_study_df[2014][130]

{'account_key': 130,
 'subscription_start': 2014,
 'total_study_hrs': 86.96879140835233,
 'lessons_completed': 8.0,
 'projects_completed': 0.0,
 'c_hrs': {'Intro to Data Science_hrs': 53.17091580556167,
  'Intro to HTML and CSS_hrs': 0.0,
  'Data Analyst Nanodegree_hrs': 3.4385773888845006,
  'Data Visualization and D3.js_hrs': 0.0,
  'Data Analysis with R_hrs': 0.0,
  'JavaScript Basics_hrs': 0.0,
  'Intro to Machine Learning_hrs': 0.0,
  'A/B Testing_hrs': 0.0,
  'Data Wrangling with MongoDB_hrs': 30.359298213906165},
 'c_lsns': {'Intro to Data Science_lesson': 9.0,
  'Intro to HTML and CSS_lesson': 0.0,
  'Data Analyst Nanodegree_lesson': 0.0,
  'Data Visualization and D3.js_lesson': 0.0,
  'Data Analysis with R_lesson': 0.0,
  'JavaScript Basics_lesson': 0.0,
  'Intro to Machine Learning_lesson': 0.0,
  'A/B Testing_lesson': 0.0,
  'Data Wrangling with MongoDB_lesson': 8.0},
 'c_proj': {'Intro to Data Science_proj': 1.0,
  'Intro to HTML and CSS_proj': 0.0,
  'Data Analyst Nanodegree_proj': 0.0,
  'Data Visualization and D3.js_proj': 0.0,
  'Data Analysis with R_proj': 0.0,
  'JavaScript Basics_proj': 0.0,
  'Intro to Machine Learning_proj': 0.0,
  'A/B Testing_proj': 0.0,
  'Data Wrangling with MongoDB_proj': 0.0}}


#users_study_df = users_study_df.astype(str)
users_study_df.dtypes

2014    object
2015    object
dtype: object


import re


def regex_df(df, new_cols):
    lst_of_dfs = []
    
    for c in df.columns:
        df_lst = []
        #df_new = pd.DataFrame(columns=new_cols)
        
        for row in df[c]:
            spare = []
            
            for col in new_cols:
                to_extract = re.compile(f"'{col}': [0-9]+((.[0-9])?)*")   # (?s:.*?)   # .[0-9]?
                #to_extract = re.match(col, str(row))
                compared = to_extract.search(str(row))
                if compared!=None:
                    spare.append(str(compared.group()[len(col)+4:]))
                else:
                    spare.append(None)
            
            df_lst.append(spare)
        
        df_new = pd.DataFrame(df_lst, columns=new_cols)
        df_new.to_csv(f'daily_engagement_{c}.csv')
        print(f"daily_engagement_{c} has been saved....")
        lst_of_dfs.append(df_new)
    return lst_of_dfs


each_year_user_lst_df = regex_df(users_study_df, meta_cols)

daily_engagement_2014 has been saved....
daily_engagement_2015 has been saved....


each_year_user_lst_df

[     account_key subscription_start     total_study_hrs lessons_completed  \
 0            448               2014   7.865357536110332               0.0   
 1             44               2014  46.394156652769325              12.0   
 2            258               2014      99.50050039167              11.0   
 3            366               2014   102.5816878972188              11.0   
 4            587               2014  17.879348477769334               0.0   
 ...          ...                ...                 ...               ...   
 1159         990               2014                 0.0               0.0   
 1160         686               2014                 0.0               0.0   
 1161        1067               2014                 0.0               0.0   
 1162         754               2014                 0.0               0.0   
 1163         854               2014                 0.0               0.0   
 
      projects_completed Intro to Data Science_hrs Intro to HTML and CSS_hrs  \
 0                   0.0         1.630967122222833             0.76440243055   
 1                   0.0            22.17087966667                       0.0   
 2                   1.0        22.683015541666666                       0.0   
 3                   0.0          33.8699802444555                       0.0   
 4                   0.0        16.004607786103833                       0.0   
 ...                 ...                       ...                       ...   
 1159                0.0                       0.0                       0.0   
 1160                0.0                       0.0                       0.0   
 1161                0.0                       0.0                       0.0   
 1162                0.0                       0.0                       0.0   
 1163                0.0                       0.0                       0.0   
 
      Data Analyst Nanodegree_hrs Data Visualization and D3.js_hrs  \
 0              3.406064944444332              0.15292225277766666   
 1              2.638166563883333                              0.0   
 2              5.058334588888333                9.445516413890001   
 3              2.968250669443833                3.994799516661166   
 4              1.617224061110500                              0.0   
 ...                          ...                              ...   
 1159                         0.0                              0.0   
 1160                         0.0                              0.0   
 1161                         0.0                              0.0   
 1162                         0.0                              0.0   
 1163                         0.0                              0.0   
 
      Data Analysis with R_hrs  ... Data Wrangling with MongoDB_lesson  \
 0         0.35808756388833335  ...                                0.0   
 1                         0.0  ...                               12.0   
 2            19.8368701444333  ...                               11.0   
 3           9.556285272220002  ...                               11.0   
 4                         0.0  ...                                0.0   
 ...                       ...  ...                                ...   
 1159                      0.0  ...                                0.0   
 1160                      0.0  ...                                0.0   
 1161                      0.0  ...                                0.0   
 1162                      0.0  ...                                0.0   
 1163                      0.0  ...                                0.0   
 
      Intro to Data Science_proj Intro to HTML and CSS_proj  \
 0                           0.0                        0.0   
 1                           1.0                        0.0   
 2                           1.0                        0.0   
 3                           1.0                        0.0   
 4                           0.0                        0.0   
 ...                         ...                        ...   
 1159                        0.0                        0.0   
 1160                        0.0                        0.0   
 1161                        0.0                        0.0   
 1162                        0.0                        0.0   
 1163                        0.0                        0.0   
 
      Data Analyst Nanodegree_proj Data Visualization and D3.js_proj  \
 0                             0.0                               0.0   
 1                             0.0                               0.0   
 2                             1.0                               1.0   
 3                             0.0                               0.0   
 4                             0.0                               0.0   
 ...                           ...                               ...   
 1159                          0.0                               0.0   
 1160                          0.0                               0.0   
 1161                          0.0                               0.0   
 1162                          0.0                               0.0   
 1163                          0.0                               0.0   
 
      Data Analysis with R_proj JavaScript Basics_proj  \
 0                          0.0                    0.0   
 1                          0.0                    0.0   
 2                          1.0                    0.0   
 3                          0.0                    0.0   
 4                          0.0                    0.0   
 ...                        ...                    ...   
 1159                       0.0                    0.0   
 1160                       0.0                    0.0   
 1161                       0.0                    0.0   
 1162                       0.0                    0.0   
 1163                       0.0                    0.0   
 
      Intro to Machine Learning_proj A/B Testing_proj  \
 0                               0.0              0.0   
 1                               0.0              0.0   
 2                               1.0              0.0   
 3                               0.0              0.0   
 4                               0.0              0.0   
 ...                             ...              ...   
 1159                            0.0              0.0   
 1160                            0.0              0.0   
 1161                            0.0              0.0   
 1162                            0.0              0.0   
 1163                            0.0              0.0   
 
      Data Wrangling with MongoDB_proj  
 0                                 0.0  
 1                                 0.0  
 2                                 1.0  
 3                                 0.0  
 4                                 0.0  
 ...                               ...  
 1159                              0.0  
 1160                              0.0  
 1161                              0.0  
 1162                              0.0  
 1163                              0.0  
 
 [1164 rows x 32 columns],
      account_key subscription_start      total_study_hrs lessons_completed  \
 0            448               2015   13.087178891668332               0.0   
 1             44               2015       24.78622549443               0.0   
 2            258               2015                  0.0               0.0   
 3            366               2015    3.230152600000166               0.0   
 4            587               2015     41.0999053833245               5.0   
 ...          ...                ...                  ...               ...   
 1159         990               2015    0.836528125001166               0.0   
 1160         686               2015  0.16020183888883333               0.0   
 1161        1067               2015    0.495628972221666               0.0   
 1162         754               2015    6.888770208328333               0.0   
 1163         854               2015  0.36795265277833333               0.0   
 
      projects_completed Intro to Data Science_hrs Intro to HTML and CSS_hrs  \
 0                   0.0       0.05284328333333333         0.332654547222166   
 1                   1.0                       0.0                       0.0   
 2                   0.0                       0.0                       0.0   
 3                   0.0         1.925212513889999                       0.0   
 4                   0.0        28.539121397216164                       0.0   
 ...                 ...                       ...                       ...   
 1159                0.0                       0.0                       0.0   
 1160                0.0                       0.0                       0.0   
 1161                0.0                       0.0                       0.0   
 1162                0.0                       0.0                       0.0   
 1163                0.0                       0.0                       0.0   
 
      Data Analyst Nanodegree_hrs Data Visualization and D3.js_hrs  \
 0               10.1521735861133                1.096988658332333   
 1              1.376195825001666              0.04586874166666666   
 2                            0.0                              0.0   
 3            0.30557437222166667                              0.0   
 4              1.250586677776666                              0.0   
 ...                          ...                              ...   
 1159           0.836528125001166                              0.0   
 1160         0.16020183888883333                              0.0   
 1161           0.495628972221666                              0.0   
 1162                         0.0                              0.0   
 1163         0.36795265277833333                              0.0   
 
      Data Analysis with R_hrs  ... Data Wrangling with MongoDB_lesson  \
 0         0.42080968611166664  ...                                0.0   
 1          15.791170974987834  ...                                0.0   
 2                         0.0  ...                                0.0   
 3                         0.0  ...                                0.0   
 4                         0.0  ...                                5.0   
 ...                       ...  ...                                ...   
 1159                      0.0  ...                                0.0   
 1160                      0.0  ...                                0.0   
 1161                      0.0  ...                                0.0   
 1162                      0.0  ...                                0.0   
 1163                      0.0  ...                                0.0   
 
      Intro to Data Science_proj Intro to HTML and CSS_proj  \
 0                           1.0                        0.0   
 1                           0.0                        0.0   
 2                           0.0                        0.0   
 3                           0.0                        0.0   
 4                           0.0                        0.0   
 ...                         ...                        ...   
 1159                        0.0                        0.0   
 1160                        0.0                        0.0   
 1161                        0.0                        0.0   
 1162                        0.0                        0.0   
 1163                        0.0                        0.0   
 
      Data Analyst Nanodegree_proj Data Visualization and D3.js_proj  \
 0                             0.0                               0.0   
 1                             0.0                               0.0   
 2                             0.0                               0.0   
 3                             0.0                               0.0   
 4                             0.0                               0.0   
 ...                           ...                               ...   
 1159                          0.0                               0.0   
 1160                          0.0                               0.0   
 1161                          0.0                               0.0   
 1162                          0.0                               0.0   
 1163                          0.0                               0.0   
 
      Data Analysis with R_proj JavaScript Basics_proj  \
 0                          0.0                    0.0   
 1                          1.0                    0.0   
 2                          0.0                    0.0   
 3                          0.0                    0.0   
 4                          0.0                    0.0   
 ...                        ...                    ...   
 1159                       0.0                    0.0   
 1160                       0.0                    0.0   
 1161                       0.0                    0.0   
 1162                       0.0                    0.0   
 1163                       0.0                    0.0   
 
      Intro to Machine Learning_proj A/B Testing_proj  \
 0                               0.0              0.0   
 1                               0.0              0.0   
 2                               0.0              0.0   
 3                               0.0              0.0   
 4                               0.0              0.0   
 ...                             ...              ...   
 1159                            0.0              0.0   
 1160                            0.0              0.0   
 1161                            0.0              0.0   
 1162                            0.0              0.0   
 1163                            0.0              0.0   
 
      Data Wrangling with MongoDB_proj  
 0                                 0.0  
 1                                 1.0  
 2                                 0.0  
 3                                 0.0  
 4                                 0.0  
 ...                               ...  
 1159                              0.0  
 1160                              0.0  
 1161                              0.0  
 1162                              0.0  
 1163                              0.0  
 
 [1164 rows x 32 columns]]


len(acct_2014)

429


len(acct_2015)

945


len(common_students)

137


df_2014 = pd.read_csv("daily_engagement_2014.csv")


df_2014


df_2015 = pd.read_csv("daily_engagement_2015.csv")


df_2015


df_2014['account_key'].tolist() == df_2015['account_key'].tolist()

True


unwanted_for_2014 = [x for x in acct_2015 if x not in acct_2014]


len(unwanted_for_2014)

808


len(unwanted_for_2014) + len(common_students) == len(acct_2015)

True


unwanted_for_2015 = [x for x in acct_2014 if x not in acct_2015]


len(unwanted_for_2015)

292


len(unwanted_for_2015) + len(common_students) == len(acct_2014)

True


len(unwanted_for_2014) + len(common_students) == len(acct_2015)

True


neglect_2014 = df_2014[df_2014['account_key'].isin(unwanted_for_2014)]


neglect_2014


df_2014.drop(neglect_2014.index, inplace=True)


df_2014


df_2014.to_csv("cleaned_2014.csv")


neglect_2015 = df_2015[df_2015['account_key'].isin(unwanted_for_2015)]


df_2015.drop(neglect_2015.index, inplace=True)


df_2015


df_2015.to_csv("cleaned_2015.csv")


cleaned_2014 = pd.read_csv('cleaned_2014.csv')
cleaned_2015 = pd.read_csv('cleaned_2015.csv')


len(cleaned_2015.columns)

34


len(cleaned_2014.columns)

34


to_plot_hrs = list(cleaned_2015.columns)[7:16]


to_plot_hrs

['Intro to Data Science_hrs',
 'Intro to HTML and CSS_hrs',
 'Data Analyst Nanodegree_hrs',
 'Data Visualization and D3.js_hrs',
 'Data Analysis with R_hrs',
 'JavaScript Basics_hrs',
 'Intro to Machine Learning_hrs',
 'A/B Testing_hrs',
 'Data Wrangling with MongoDB_hrs']


to_plot_lsns = list(cleaned_2015.columns)[16:25]


to_plot_lsns

['Intro to Data Science_lesson',
 'Intro to HTML and CSS_lesson',
 'Data Analyst Nanodegree_lesson',
 'Data Visualization and D3.js_lesson',
 'Data Analysis with R_lesson',
 'JavaScript Basics_lesson',
 'Intro to Machine Learning_lesson',
 'A/B Testing_lesson',
 'Data Wrangling with MongoDB_lesson']


to_plot_projs = list(cleaned_2015.columns)[25:]


to_plot_projs

['Intro to Data Science_proj',
 'Intro to HTML and CSS_proj',
 'Data Analyst Nanodegree_proj',
 'Data Visualization and D3.js_proj',
 'Data Analysis with R_proj',
 'JavaScript Basics_proj',
 'Intro to Machine Learning_proj',
 'A/B Testing_proj',
 'Data Wrangling with MongoDB_proj']


def plot_progress(df, col1, cols_lst_hrs, cols_lst_lsns, cols_lst_proj, num):
    numbr = [i+1 for i in range(len(cols_lst_hrs))]
    for hrs, lsns, proj, n in zip(cols_lst_hrs, cols_lst_lsns, cols_lst_proj, numbr):
        fig, axes = plt.subplots(1, 3, figsize=(11, 5))
        title = hrs.split('_')[0]
        fig.suptitle(title, fontsize=16)
        
        df_hrs = df.sort_values(by=[hrs])
        df_lsns = df.sort_values(by=[lsns])
        df_proj = df.sort_values(by=[proj])
        
        toPlot_hrs = df_hrs[hrs].tail(num)
        toPlot_lsns = df_lsns[lsns].tail(num)
        toPlot_proj = df_proj[proj].tail(num)
        
        toPlot_hrs.plot(ax=axes[0], kind='bar', title=title, figsize=(10, 6))
        toPlot_lsns.plot(ax=axes[1], kind='bar', title=title, figsize=(10, 6))
        toPlot_proj.plot(ax=axes[2], kind='bar', title=title, figsize=(10, 6))
        
        axes[0].set_title(hrs.split('_')[1])
        axes[1].set_title(lsns.split('_')[1])
        axes[2].set_title(proj.split('_')[1])
        plt.savefig(f'{n}.png', facecolor='w', bbox_inches="tight", pad_inches=0.3, transparent=False)
        plt.show()
        #plt.xlabel(col1)
        #plt.ylabel(col2)
        #plt.show()
        print(toPlot_hrs, toPlot_lsns, toPlot_proj)


plot_progress(cleaned_2014, 'account_key', to_plot_hrs, to_plot_lsns, to_plot_projs, 5)

122    72.939193
216    75.020808
71     83.520889
74     85.327424
241    87.144486
Name: Intro to Data Science_hrs, dtype: float64 16     10.0
335    10.0
282    10.0
304    10.0
260    10.0
Name: Intro to Data Science_lesson, dtype: float64 261    1.0
263    1.0
264    1.0
100    1.0
82     1.0
Name: Intro to Data Science_proj, dtype: float64

158    5.808720
102    6.232276
48     7.137908
347    7.861887
121    8.349262
Name: Intro to HTML and CSS_hrs, dtype: float64 48     3.0
154    3.0
343    3.0
117    3.0
100    3.0
Name: Intro to HTML and CSS_lesson, dtype: float64 130    0.0
140    0.0
409    0.0
48     1.0
407    1.0
Name: Intro to HTML and CSS_proj, dtype: float64

207     9.080338
360     9.154015
91      9.258947
42     10.848733
237    10.874616
Name: Data Analyst Nanodegree_hrs, dtype: float64 91     2.0
264    2.0
13     2.0
360    3.0
237    3.0
Name: Data Analyst Nanodegree_lesson, dtype: float64 42     1.0
264    1.0
158    1.0
121    1.0
154    1.0
Name: Data Analyst Nanodegree_proj, dtype: float64

121    28.997682
328    29.093131
115    34.349993
216    34.425644
101    41.181727
Name: Data Visualization and D3.js_hrs, dtype: float64 24     8.0
38     8.0
97     8.0
268    8.0
77     8.0
Name: Data Visualization and D3.js_lesson, dtype: float64 89     1.0
268    1.0
121    1.0
22     1.0
77     1.0
Name: Data Visualization and D3.js_proj, dtype: float64

108    50.604694
216    52.406257
29     58.416228
308    61.452862
93     64.785145
Name: Data Analysis with R_hrs, dtype: float64 207    9.0
303    9.0
77     9.0
201    9.0
286    9.0
Name: Data Analysis with R_lesson, dtype: float64 107    1.0
108    1.0
111    1.0
306    1.0
94     1.0
Name: Data Analysis with R_proj, dtype: float64

324    5.920621
48     6.367822
158    7.172721
24     7.707952
121    8.310206
Name: JavaScript Basics_hrs, dtype: float64 24     6.0
324    6.0
343    7.0
48     7.0
154    7.0
Name: JavaScript Basics_lesson, dtype: float64 130    0.0
140    0.0
409    0.0
48     1.0
407    1.0
Name: JavaScript Basics_proj, dtype: float64

42     55.472784
121    57.905742
93     76.009287
216    89.215894
308    93.494506
Name: Intro to Machine Learning_hrs, dtype: float64 100    14.0
101    14.0
102    14.0
308    14.0
93     14.0
Name: Intro to Machine Learning_lesson, dtype: float64 89     1.0
264    1.0
50     1.0
42     1.0
115    1.0
Name: Intro to Machine Learning_proj, dtype: float64

24      8.757659
74     10.734904
107    11.776014
102    16.290011
101    22.784276
Name: A/B Testing_hrs, dtype: float64 347    4.0
101    4.0
24     4.0
207    5.0
102    5.0
Name: A/B Testing_lesson, dtype: float64 132    0.0
131    0.0
130    0.0
140    0.0
409    0.0
Name: A/B Testing_proj, dtype: float64

201     64.806271
358     75.069440
29      94.013464
308     94.702774
74     115.771617
Name: Data Wrangling with MongoDB_hrs, dtype: float64 313    11.0
327    11.0
79     11.0
181    12.0
1      12.0
Name: Data Wrangling with MongoDB_lesson, dtype: float64 98     1.0
279    1.0
280    1.0
282    1.0
91     1.0
Name: Data Wrangling with MongoDB_proj, dtype: float64

	acct	has_visited	total_minutes_visited	lessons_completed	projects_completed
count	2.309239e+06	2.309239e+06	2.309239e+06	2.309239e+06	2.309239e+06
mean	4.496441e+02	2.012178e-02	1.448495e+00	7.598607e-03	3.971005e-04
std	3.215611e+02	1.404169e-01	1.525523e+01	1.128178e-01	1.992343e-02
min	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00
25%	2.010000e+02	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00
50%	3.940000e+02	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00
75%	5.960000e+02	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00
max	1.305000e+03	1.000000e+00	7.371061e+02	1.600000e+01	1.000000e+00

	2014	2015
448	{'account_key': 448, 'subscription_start': 201...	{'account_key': 448, 'subscription_start': 201...
44	{'account_key': 44, 'subscription_start': 2014...	{'account_key': 44, 'subscription_start': 2015...
258	{'account_key': 258, 'subscription_start': 201...	{'account_key': 258, 'subscription_start': 201...
366	{'account_key': 366, 'subscription_start': 201...	{'account_key': 366, 'subscription_start': 201...
587	{'account_key': 587, 'subscription_start': 201...	{'account_key': 587, 'subscription_start': 201...
...	...	...
990	{'account_key': 990, 'subscription_start': 201...	{'account_key': 990, 'subscription_start': 201...
686	{'account_key': 686, 'subscription_start': 201...	{'account_key': 686, 'subscription_start': 201...
1067	{'account_key': 1067, 'subscription_start': 20...	{'account_key': 1067, 'subscription_start': 20...
754	{'account_key': 754, 'subscription_start': 201...	{'account_key': 754, 'subscription_start': 201...
854	{'account_key': 854, 'subscription_start': 201...	{'account_key': 854, 'subscription_start': 201...

Step 1: Investigating the dataset¶

it seems that both columns 'acct' and 'account_key' represent the same data so let's build a function to check that¶

Step 2: Preprocessing¶

So we can dispense with one of the two columns: 'acct' and 'account_key', I prefer to drop 'account key' as it has long numbers, and I'll rename 'acct' into 'account_key'¶

It seems that the same values are common between the 'sibling_key' and 'course_key'¶

it seems that both columns 'course_title' and 'sibling_key' represent the same data so let's check that using represent_same¶

it seems that there's more than one 'course_key' for each 'course_title', so let's ensure this with a function¶

So we can dispense with both: 'course_key' and 'sibling_key', as both just refer to the course title and gives no more needed details¶

it seems that both columns 'course_title' and 'course_key' represent the same data so let's check that¶

Actually 'total_minutes_visited' column represents also the 'has_visited' column, so we can remove the 'has_visited' column¶

unknown_courses_df (52965) percentage represents 2.32% from the whole data (2275075)¶

In terms of the top most important topics we can take the most common four nanodegrees as follows:¶

Now let's see what's the most year that the user's achieved in their courses¶

Actually for 'registration_date' whrn year was 1816 there wasn't any technology yet, bad input so I'm going to drop it¶

Get common users for both subscription years: 2014, 2015¶

So now there's a three categories of students that can extract data according to¶

Some students doesn't achieve anything in the columns of 'total_minutes_visited', 'lessons_completed', 'projects_completed'¶

let's check which year have the highest number of inactive students¶

all the students are unstable for their nanodegrees material studying so we can neglect those data from the dataframe as it's huge and doesn't add any more information than that¶

let's get students who subscribes in nanodegrees and not do any progress in it¶

They are the 'account_key' for the users that didn't study anything or do any projects for any of the nanodegrees¶

We can now use regular expression in python to extract the wanted data¶

Now we have two .csv files contains the student progress per each year starting from the subscribtion date¶

Now let's plot some plots for the students according to their achievements in both years¶

	utc_date	acct	registration_date	subscription_start	course_key	sibling_key	course_title	account_key
0	2014-11-05	448	2014-08-05	2014-11-05	ud359-nd	ud359	Intro to Data Science	2257038596
1	2014-11-05	448	2014-08-05	2014-11-05	ud120-nd	ud120	Intro to Machine Learning	2257038596
2	2014-11-05	448	2014-08-05	2014-11-05	ud651-nd	ud651	Data Analysis with R	2257038596
3	2014-11-05	448	2014-08-05	2014-11-05	ud507-nd	ud507	Data Visualization and D3.js	2257038596
4	2014-11-05	448	2014-08-05	2014-11-05	ud651	ud651	Data Analysis with R	2257038596

	count	mean	std	25%	50%	75%	max
acct	2309239.0	449.644054	321.561123	201.0	394.0	596.0	1305.000000
has_visited	2309239.0	0.020122	0.140417	0.0	0.0	0.0	1.000000
total_minutes_visited	2309239.0	1.448495	15.255230	0.0	0.0	0.0	737.106143
lessons_completed	2309239.0	0.007599	0.112818	0.0	0.0	0.0	16.000000
projects_completed	2309239.0	0.000397	0.019923	0.0	0.0	0.0	1.000000

	utc_date	acct	registration_date	subscription_start	course_key	sibling_key	course_title	has_visited	total_minutes_visited	lessons_completed	projects_completed	account_key
3911	2015-07-09	448	2014-08-05	2015-03-10	ud359-nd	ud359	Intro to Data Science	0.0	0.000000	0.0	0.0	2257038596
3912	2015-07-09	448	2014-08-05	2015-03-10	ud651-nd	ud651	Data Analysis with R	0.0	0.000000	0.0	0.0	2257038596
3913	2015-07-09	448	2014-08-05	2015-03-10	ud032-nd	ud032	Data Wrangling with MongoDB	0.0	0.000000	0.0	0.0	2257038596
3914	2015-07-09	448	2014-08-05	2015-03-10	ud120-nd	ud120	Intro to Machine Learning	0.0	0.000000	0.0	0.0	2257038596
3915	2015-07-09	448	2014-08-05	2015-03-10	ud507-nd	ud507	Data Visualization and D3.js	0.0	0.000000	0.0	0.0	2257038596
...	...	...	...	...	...	...	...	...	...	...	...	...
2260954	2015-07-11	813	2015-06-19	2015-07-11	ud032	ud032	Data Wrangling with MongoDB	0.0	0.000000	0.0	0.0	4495968580
2260955	2015-07-11	813	2015-06-19	2015-07-11	ud507	ud507	Data Visualization and D3.js	0.0	0.000000	0.0	0.0	4495968580
2260956	2015-07-11	813	2015-06-19	2015-07-11	ud304	ud304	Intro to HTML and CSS	0.0	0.000000	0.0	0.0	4495968580
2260957	2015-07-11	813	2015-06-19	2015-07-11	ud651	ud651	Data Analysis with R	0.0	0.000000	0.0	0.0	4495968580
2260958	2015-07-11	813	2015-06-19	2015-07-11	nd002	nd002	Data Analyst Nanodegree	1.0	52.856401	0.0	0.0	4495968580

		utc_date	registration_date	subscription_start	course_key	sibling_key	has_visited	total_minutes_visited	lessons_completed	projects_completed
account_key	course_title
0	A/B Testing	2015-03-05	2015-01-09	2015-01-09	ud257-nd	ud257	0.0	0.000000	0.0	0.0
	Data Analysis with R	2015-01-09	2015-01-09	2015-01-09	ud651-nd	ud651	0.0	0.000000	0.0	0.0
	Data Analyst Nanodegree	2015-01-09	2015-01-09	2015-01-09	nd002	nd002	1.0	11.679374	0.0	0.0
	Data Visualization and D3.js	2015-01-09	2015-01-09	2015-01-09	ud507-nd	ud507	0.0	0.000000	0.0	0.0
	Data Wrangling with MongoDB	2015-01-09	2015-01-09	2015-01-09	ud032	ud032	0.0	0.000000	0.0	0.0
...	...	...	...	...	...	...	...	...	...	...
1305	Data Wrangling with MongoDB	2015-05-12	2014-10-31	2015-05-12	ud032-nd	ud032	0.0	0.000000	0.0	0.0
	Intro to Data Science	2015-05-12	2014-10-31	2015-05-12	ud359-nd	ud359	0.0	0.000000	0.0	0.0
	Intro to HTML and CSS	2015-05-12	2014-10-31	2015-05-12	ud304-nd	ud304	0.0	0.000000	0.0	0.0
	Intro to Machine Learning	2015-05-12	2014-10-31	2015-05-12	ud120-nd	ud120	0.0	0.000000	0.0	0.0
	JavaScript Basics	2015-05-12	2014-10-31	2015-05-12	ud804-nd	ud804	0.0	0.000000	0.0	0.0

		utc_date	account_key	registration_date	subscription_start	sibling_key	has_visited	total_minutes_visited	lessons_completed	projects_completed
course_title	course_key
A/B Testing	ud257	2015-03-05	448	2014-08-05	2015-01-27	ud257	0.0	0.0	0.0	0.0
A/B Testing	ud257-nd	2015-03-05	448	2014-08-05	2015-01-27	ud257	0.0	0.0	0.0	0.0
Data Analysis with R	ud651	2014-11-05	448	2014-08-05	2014-11-05	ud651	0.0	0.0	0.0	0.0
Data Analysis with R	ud651-nd	2014-11-05	448	2014-08-05	2014-11-05	ud651	0.0	0.0	0.0	0.0
Data Analyst Nanodegree	nd002	2014-11-05	448	2014-08-05	2014-11-05	nd002	0.0	0.0	0.0	0.0
Data Visualization and D3.js	ud507	2014-11-05	448	2014-08-05	2014-11-05	ud507	0.0	0.0	0.0	0.0
Data Visualization and D3.js	ud507-nd	2014-11-05	448	2014-08-05	2014-11-05	ud507	0.0	0.0	0.0	0.0
Data Wrangling with MongoDB	ud032	2014-11-05	448	2014-08-05	2014-11-05	ud032	0.0	0.0	0.0	0.0
Data Wrangling with MongoDB	ud032-nd	2014-11-05	448	2014-08-05	2014-11-05	ud032	0.0	0.0	0.0	0.0
Intro to Data Science	ud359	2014-11-05	448	2014-08-05	2014-11-05	ud359	0.0	0.0	0.0	0.0
Intro to Data Science	ud359-nd	2014-11-05	448	2014-08-05	2014-11-05	ud359	0.0	0.0	0.0	0.0
Intro to HTML and CSS	ud304	2014-11-05	448	2014-08-05	2014-11-05	ud304	0.0	0.0	0.0	0.0
Intro to HTML and CSS	ud304-nd	2014-11-05	448	2014-08-05	2014-11-05	ud304	0.0	0.0	0.0	0.0
Intro to Machine Learning	ud120	2014-11-05	448	2014-08-05	2014-11-05	ud120	0.0	0.0	0.0	0.0
Intro to Machine Learning	ud120-nd	2014-11-05	448	2014-08-05	2014-11-05	ud120	0.0	0.0	0.0	0.0
JavaScript Basics	ud804	2014-11-05	448	2014-08-05	2014-11-05	ud804	0.0	0.0	0.0	0.0
JavaScript Basics	ud804-nd	2014-11-05	448	2014-08-05	2014-11-05	ud804	0.0	0.0	0.0	0.0
ud134-nd	ud134-nd	2015-07-04	448	2014-08-05	2015-03-10	ud134	0.0	0.0	0.0	0.0
ud134a-nd	ud134a-nd	2015-07-30	448	2014-08-05	2015-03-10	ud134a	0.0	0.0	0.0	0.0

	utc_date	account_key	registration_date	subscription_start	course_title	total_minutes_visited	lessons_completed	projects_completed
3811	2015-07-04	448	2014-08-05	2015-03-10	ud134-nd	0.000000	0.0	0.0
3822	2015-07-05	448	2014-08-05	2015-03-10	ud134-nd	0.000000	0.0	0.0
3845	2015-07-06	448	2014-08-05	2015-03-10	ud134-nd	0.000000	0.0	0.0
3864	2015-07-07	448	2014-08-05	2015-03-10	ud134-nd	0.000000	0.0	0.0
3881	2015-07-08	448	2014-08-05	2015-03-10	ud134-nd	0.000000	0.0	0.0
...	...	...	...	...	...	...	...	...
2309192	2015-08-24	854	2012-10-12	2015-08-23	ud134a-nd	0.000000	0.0	0.0
2309206	2015-08-25	854	2012-10-12	2015-08-23	ud134-nd	0.000000	0.0	0.0
2309207	2015-08-25	854	2012-10-12	2015-08-23	ud134a-nd	0.000000	0.0	0.0
2309226	2015-08-26	854	2012-10-12	2015-08-23	ud134-nd	0.000000	0.0	0.0
2309233	2015-08-26	854	2012-10-12	2015-08-23	ud134a-nd	42.222797	1.0	0.0

	utc_date	account_key	registration_date	subscription_start	course_title	total_minutes_visited
2308289	2015-08-23	880	1816-03-30	2015-08-23	Intro to Machine Learning	0.000000
2308290	2015-08-23	880	1816-03-30	2015-08-23	Data Visualization and D3.js	0.000000
2308291	2015-08-23	880	1816-03-30	2015-08-23	Data Wrangling with MongoDB	0.000000
2308292	2015-08-23	880	1816-03-30	2015-08-23	Intro to HTML and CSS	0.000000
2308293	2015-08-23	880	1816-03-30	2015-08-23	JavaScript Basics	0.000000
2308294	2015-08-23	880	1816-03-30	2015-08-23	Intro to Data Science	15.593026
2308295	2015-08-23	880	1816-03-30	2015-08-23	Data Analysis with R	0.000000
2308296	2015-08-23	880	1816-03-30	2015-08-23	A/B Testing	0.000000
2308297	2015-08-23	880	1816-03-30	2015-08-23	Intro to Machine Learning	0.000000
2308298	2015-08-23	880	1816-03-30	2015-08-23	A/B Testing	0.000000
2308301	2015-08-23	880	1816-03-30	2015-08-23	JavaScript Basics	0.000000
2308302	2015-08-23	880	1816-03-30	2015-08-23	Intro to HTML and CSS	0.000000
2308303	2015-08-23	880	1816-03-30	2015-08-23	Data Wrangling with MongoDB	0.000000
2308304	2015-08-23	880	1816-03-30	2015-08-23	Intro to Data Science	0.000000
2308305	2015-08-23	880	1816-03-30	2015-08-23	Data Analysis with R	0.000000
2308306	2015-08-23	880	1816-03-30	2015-08-23	Data Visualization and D3.js	0.000000
2308307	2015-08-23	880	1816-03-30	2015-08-23	Data Analyst Nanodegree	0.000000
2308308	2015-08-24	880	1816-03-30	2015-08-23	Data Wrangling with MongoDB	0.000000
2308309	2015-08-24	880	1816-03-30	2015-08-23	Intro to Data Science	0.000000
2308310	2015-08-24	880	1816-03-30	2015-08-23	Data Analysis with R	0.000000
2308311	2015-08-24	880	1816-03-30	2015-08-23	JavaScript Basics	0.000000
2308312	2015-08-24	880	1816-03-30	2015-08-23	Data Visualization and D3.js	0.000000
2308313	2015-08-24	880	1816-03-30	2015-08-23	Intro to Machine Learning	0.000000
2308315	2015-08-24	880	1816-03-30	2015-08-23	A/B Testing	0.000000
2308317	2015-08-24	880	1816-03-30	2015-08-23	A/B Testing	0.000000
2308318	2015-08-24	880	1816-03-30	2015-08-23	Intro to HTML and CSS	0.000000
2308319	2015-08-24	880	1816-03-30	2015-08-23	Data Analysis with R	0.000000
2308320	2015-08-24	880	1816-03-30	2015-08-23	Intro to Machine Learning	0.000000
2308321	2015-08-24	880	1816-03-30	2015-08-23	Data Wrangling with MongoDB	0.000000
2308322	2015-08-24	880	1816-03-30	2015-08-23	Data Analyst Nanodegree	0.000000
2308323	2015-08-24	880	1816-03-30	2015-08-23	Data Visualization and D3.js	0.000000
2308324	2015-08-24	880	1816-03-30	2015-08-23	Intro to Data Science	0.000000
2308325	2015-08-24	880	1816-03-30	2015-08-23	Intro to HTML and CSS	0.000000
2308326	2015-08-24	880	1816-03-30	2015-08-23	JavaScript Basics	0.000000

	utc_date	account_key	registration_date	subscription_start	course_title	total_minutes_visited	lessons_completed	projects_completed
75	2014-11-10	448	2014-08-05	2014-11-10	Intro to Data Science	4.201422	0.0	0.0
80	2014-11-10	448	2014-08-05	2014-11-10	Intro to HTML and CSS	21.863585	0.0	0.0
89	2014-11-10	448	2014-08-05	2014-11-10	Data Analyst Nanodegree	4.681858	0.0	0.0
129	2014-11-13	448	2014-08-05	2014-11-10	Intro to Data Science	14.644702	0.0	0.0
132	2014-11-13	448	2014-08-05	2014-11-10	Data Visualization and D3.js	4.972973	0.0	0.0
...	...	...	...	...	...	...	...	...
2309085	2015-08-23	754	2015-01-22	2015-08-21	Intro to Machine Learning	23.399109	0.0	0.0
2309124	2015-08-25	754	2015-01-22	2015-08-21	Intro to Machine Learning	40.125426	0.0	0.0
2309142	2015-08-26	754	2015-01-22	2015-08-21	Intro to Machine Learning	68.243253	0.0	0.0
2309162	2015-08-27	754	2015-01-22	2015-08-21	Intro to Machine Learning	281.558425	2.0	0.0
2309215	2015-08-25	854	2012-10-12	2015-08-23	Data Analyst Nanodegree	22.077159	1.0	0.0

	Unnamed: 0	account_key	subscription_start	total_study_hrs	lessons_completed	projects_completed	Intro to Data Science_hrs	Intro to HTML and CSS_hrs	Data Analyst Nanodegree_hrs	Data Visualization and D3.js_hrs	...	Data Wrangling with MongoDB_lesson	Intro to Data Science_proj	Intro to HTML and CSS_proj	Data Analyst Nanodegree_proj	Data Visualization and D3.js_proj	Data Analysis with R_proj	JavaScript Basics_proj	Intro to Machine Learning_proj	A/B Testing_proj	Data Wrangling with MongoDB_proj
0	0	448	2014	7.865358	0.0	0.0	1.630967	0.764402	3.406065	0.152922	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
1	1	44	2014	46.394157	12.0	0.0	22.170880	0.000000	2.638167	0.000000	...	12.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
2	2	258	2014	99.500500	11.0	1.0	22.683016	0.000000	5.058335	9.445516	...	11.0	1.0	0.0	1.0	1.0	1.0	0.0	1.0	0.0	1.0
3	3	366	2014	102.581688	11.0	0.0	33.869980	0.000000	2.968251	3.994800	...	11.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
4	4	587	2014	17.879348	0.0	0.0	16.004608	0.000000	1.617224	0.000000	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1159	1159	990	2014	0.000000	0.0	0.0	0.000000	0.000000	0.000000	0.000000	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
1160	1160	686	2014	0.000000	0.0	0.0	0.000000	0.000000	0.000000	0.000000	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
1161	1161	1067	2014	0.000000	0.0	0.0	0.000000	0.000000	0.000000	0.000000	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
1162	1162	754	2014	0.000000	0.0	0.0	0.000000	0.000000	0.000000	0.000000	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
1163	1163	854	2014	0.000000	0.0	0.0	0.000000	0.000000	0.000000	0.000000	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0

	Unnamed: 0	account_key	subscription_start	total_study_hrs	lessons_completed	projects_completed	Intro to Data Science_hrs	Intro to HTML and CSS_hrs	Data Analyst Nanodegree_hrs	Data Visualization and D3.js_hrs	...	Data Wrangling with MongoDB_lesson	Intro to Data Science_proj	Intro to HTML and CSS_proj	Data Analyst Nanodegree_proj	Data Visualization and D3.js_proj	Data Analysis with R_proj	JavaScript Basics_proj	Intro to Machine Learning_proj	A/B Testing_proj	Data Wrangling with MongoDB_proj
0	0	448	2015	13.087179	0.0	0.0	0.052843	0.332655	10.152174	1.096989	...	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
1	1	44	2015	24.786225	0.0	1.0	0.000000	0.000000	1.376196	0.045869	...	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	1.0
2	2	258	2015	0.000000	0.0	0.0	0.000000	0.000000	0.000000	0.000000	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
3	3	366	2015	3.230153	0.0	0.0	1.925213	0.000000	0.305574	0.000000	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
4	4	587	2015	41.099905	5.0	0.0	28.539121	0.000000	1.250587	0.000000	...	5.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1159	1159	990	2015	0.836528	0.0	0.0	0.000000	0.000000	0.836528	0.000000	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
1160	1160	686	2015	0.160202	0.0	0.0	0.000000	0.000000	0.160202	0.000000	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
1161	1161	1067	2015	0.495629	0.0	0.0	0.000000	0.000000	0.495629	0.000000	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
1162	1162	754	2015	6.888770	0.0	0.0	0.000000	0.000000	0.000000	0.000000	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
1163	1163	854	2015	0.367953	0.0	0.0	0.000000	0.000000	0.367953	0.000000	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0

Step 1: Investigating the dataset¶

it seems that both columns 'acct' and 'account_key' represent the same data so let's build a function to check that¶

Step 2: Preprocessing¶

So we can dispense with one of the two columns: 'acct' and 'account_key', I prefer to drop 'account key' as it has long numbers, and I'll rename 'acct' into 'account_key'¶

It seems that the same values are common between the 'sibling_key' and 'course_key'¶

it seems that both columns 'course_title' and 'sibling_key' represent the same data so let's check that using represent_same¶

it seems that there's more than one 'course_key' for each 'course_title', so let's ensure this with a function¶

So we can dispense with both: 'course_key' and 'sibling_key', as both just refer to the course title and gives no more needed details¶

it seems that both columns 'course_title' and 'course_key' represent the same data so let's check that¶

Actually 'total_minutes_visited' column represents also the 'has_visited' column, so we can remove the 'has_visited' column¶

unknown_courses_df (52965) percentage represents 2.32% from the whole data (2275075)¶

In terms of the top most important topics we can take the most common four nanodegrees as follows:¶

Now let's see what's the most year that the user's achieved in their courses¶

Actually for 'registration_date' whrn year was 1816 there wasn't any technology yet, bad input so I'm going to drop it¶

Get common users for both subscription years: 2014, 2015¶

Also get the users that are related to each year¶

So now there's a three categories of students that can extract data according to¶

Some students doesn't achieve anything in the columns of 'total_minutes_visited', 'lessons_completed', 'projects_completed'¶

let's check which year have the highest number of inactive students¶

all the students are unstable for their nanodegrees material studying so we can neglect those data from the dataframe as it's huge and doesn't add any more information than that¶

let's get students who subscribes in nanodegrees and not do any progress in it¶

They are the 'account_key' for the users that didn't study anything or do any projects for any of the nanodegrees¶

We can now use regular expression in python to extract the wanted data¶

Now we have two .csv files contains the student progress per each year starting from the subscribtion date¶

Now let's plot some plots for the students according to their achievements in both years¶